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WEBI
2009
Springer
15 years 4 months ago
Adapting Reinforcement Learning for Trust: Effective Modeling in Dynamic Environments
—In open multiagent systems, agents need to model their environments in order to identify trustworthy agents. Models of the environment should be accurate so that decisions about...
Özgür Kafali, Pinar Yolum
ROBOCUP
2007
Springer
102views Robotics» more  ROBOCUP 2007»
15 years 3 months ago
Heuristic Reinforcement Learning Applied to RoboCup Simulation Agents
This paper describes the design and implementation of robotic agents for the RoboCup Simulation 2D category that learns using a recently proposed Heuristic Reinforcement Learning a...
Luiz A. Celiberto, Carlos H. C. Ribeiro, Anna Hele...
AGENTS
2001
Springer
15 years 1 months ago
A social reinforcement learning agent
We report on the use of reinforcement learning with Cobot, a software agent residing in the wellknown online community LambdaMOO. Our initial work on Cobot (Isbell et al.2000) pro...
Charles Lee Isbell Jr., Christian R. Shelton, Mich...
CORR
2007
Springer
73views Education» more  CORR 2007»
14 years 9 months ago
Universal Reinforcement Learning
—We consider an agent interacting with an unmodeled environment. At each time, the agent makes an observation, takes an action, and incurs a cost. Its actions can influence futu...
Vivek F. Farias, Ciamac Cyrus Moallemi, Tsachy Wei...
GECCO
2006
Springer
133views Optimization» more  GECCO 2006»
15 years 1 months ago
On-line evolutionary computation for reinforcement learning in stochastic domains
In reinforcement learning, an agent interacting with its environment strives to learn a policy that specifies, for each state it may encounter, what action to take. Evolutionary c...
Shimon Whiteson, Peter Stone